Class "CoImp"

Description

A class for CoImp and its extensions

Objects from the Class

Objects can be created by calls of the form new("CoImp", ...).

Slots

Missing.data.matrix:

Object of class "matrix". Original missing data matrix to be imputed.

Perc.miss:

Object of class "matrix". Missing and available data percentage for each variable.

Estimated.Model:

Object of class "list". The list contains:

model	the copula model selected and estimated on the complete cases.
dimension	the dimension of the model.
parameter	the estimated dependence parameter of the model.
number	the index of the estimated model in the list of models given in input.

Estimation.Method:

Object of class "character". The estimation method used for the copula model in Estimated.Model. Allowed methods are in fitCopula.

Index.matrix.NA:

Object of class "matrix". Matrix of row and column indexes of missing data.

Smooth.param:

Object of class "numeric". The values of the nearest neighbor component of the smoothing parameter of the lp function.

Imputed.data.matrix

Object of class "matrix". The imputed data matrix.

Estimated.Model.Imp

Object of class "list". The list contains:

model	the copula model selected and estimated on the imputed cases.
dimension	the dimension of the model.
parameter	the estimated dependence parameter of the model.
number	the index of the estimated model in the list of models given in input.

Estimation.Method.Imp

Object of class "character".The estimation method used for the copula model in Estimated.Model.Imp. Allowed methods are in fitCopula.

Methods

plot

signature(x = "CoImp", y = "missing"): ...

show

signature(object = "CoImp"): ...

Author(s)

Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>,

Simone Giannerini <simone.giannerini@unibo.it>

References

Di Lascio, F.M.L. Giannerini, S. and Reale A. (201x) "A multivariate technique based on conditional copula specification for the imputation of complex dependent data". Working paper.

Di Lascio, F.M.L. Giannerini, S. and Reale A. (201x) "Exploring Copulas for the Imputation of Complex Dependent Data". Under review.

Bianchi, G. Di Lascio, F.M.L. Giannerini, S. Manzari, A. Reale, A. and Ruocco, G. (2009) "Exploring copulas for the imputation of missing nonlinearly dependent data". Proceedings of the VII Meeting Classification and Data Analysis Group of the Italian Statistical Society (Cladag), Editors: Salvatore Ingrassia and Roberto Rocci, Cleup, p. 429-432. ISBN: 978-88-6129-406-6.

See Also

See Also CoImp, lp and copula.

Examples

showClass("CoImp")

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(CoImp)
Loading required package: copula
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/CoImp/CoImp-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: CoImp-class
> ### Title: Class "CoImp"
> ### Aliases: CoImp-class show,CoImp-method plot,CoImp,missing-method
> ### Keywords: classes
> 
> ### ** Examples
> 
> showClass("CoImp")
Class "CoImp" [package "CoImp"]

Slots:
                                                                        
Name:    Missing.data.matrix             Perc.miss       Estimated.Model
Class:                matrix                matrix                  list
                                                                        
Name:      Estimation.Method       Index.matrix.NA          Smooth.param
Class:             character                matrix                vector
                                                                        
Name:    Imputed.data.matrix   Estimated.Model.Imp Estimation.Method.Imp
Class:                matrix                  list             character
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>